{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "671a3715",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "!pip install -Uqq nixtla"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83517172",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "from nixtla.utils import in_colab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00464b5b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide \n",
    "IN_COLAB = in_colab()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "991f703b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    from nixtla.utils import colab_badge\n",
    "    from dotenv import load_dotenv"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42bb08a4-4ce5-4022-bb65-041c0cdf8890",
   "metadata": {},
   "source": [
    "# Holidays and special dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e84c2ca-7dc1-4c48-8dca-5e170a397ed2",
   "metadata": {},
   "source": [
    "Calendar variables and special dates are one of the most common types of additional variables used in forecasting applications. They provide additional context on the current state of the time series, especially for window-based models such as TimeGPT-1. These variables often include adding information on each observation's month, week, day, or hour. For example, in high-frequency hourly data, providing the current month of the year provides more context than the limited history available in the input window to improve the forecasts.\n",
    "\n",
    "In this tutorial we will show how to add calendar variables automatically to a dataset using the `date_features` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "553a8be7-7664-4dff-a232-e512e5eb26a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "[![](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Nixtla/nixtla/blob/main/nbs/docs/tutorials/02_holidays.ipynb)"
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#| echo: false\n",
    "if not IN_COLAB:\n",
    "    load_dotenv()    \n",
    "    colab_badge('docs/tutorials/02_holidays')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46efe990",
   "metadata": {},
   "source": [
    "## 1. Import packages\n",
    "First, we import the required packages and initialize the Nixtla client."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd410558-513e-4eb9-ae95-fcca358fa2b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from nixtla import NixtlaClient"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ba19c7b-8222-4114-a1c2-6e9ea52f4a1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "nixtla_client = NixtlaClient(\n",
    "    # defaults to os.environ.get(\"NIXTLA_API_KEY\")\n",
    "    api_key = 'my_api_key_provided_by_nixtla'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e325b27",
   "metadata": {},
   "source": [
    "> 👍 Use an Azure AI endpoint\n",
    "> \n",
    "> To use an Azure AI endpoint, remember to set also the `base_url` argument:\n",
    "> \n",
    "> `nixtla_client = NixtlaClient(base_url=\"you azure ai endpoint\", api_key=\"your api_key\")`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c4f11ff-6190-48f9-8cc7-b8c599b0de33",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "if not IN_COLAB:\n",
    "    nixtla_client = NixtlaClient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf43e0a1",
   "metadata": {},
   "source": [
    "## 2. Load data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb3adafb-65e6-44b4-b1e9-0e12b5ff46ed",
   "metadata": {},
   "source": [
    "We will use a Google trends dataset on chocolate, with monthly data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdcbffe4-add7-4087-86c0-71a584191080",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/Nixtla/transfer-learning-time-series/main/datasets/google_trend_chocolate.csv')\n",
    "df['month'] = pd.to_datetime(df['month']).dt.to_period('M').dt.to_timestamp('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f8c709b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>chocolate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2004-01-31</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2004-02-29</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2004-03-31</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2004-04-30</td>\n",
       "      <td>30</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004-05-31</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  chocolate\n",
       "0 2004-01-31         35\n",
       "1 2004-02-29         45\n",
       "2 2004-03-31         28\n",
       "3 2004-04-30         30\n",
       "4 2004-05-31         29"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "870d9cd5",
   "metadata": {},
   "source": [
    "## 3. Forecasting with holidays and special dates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cbcded8",
   "metadata": {},
   "source": [
    "Given the predominance usage of calendar variables, we included an automatic creation of common calendar variables to the forecast method as a pre-processing step. Let's create a future dataframe that contains the upcoming holidays in the United States."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "341da194",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create future dataframe with exogenous features\n",
    "\n",
    "start_date = '2024-05'\n",
    "dates = pd.date_range(start=start_date, periods=14, freq='M')\n",
    "\n",
    "dates = dates.to_period('M').to_timestamp('M')\n",
    "\n",
    "future_df = pd.DataFrame(dates, columns=['month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f0af006e",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>month</th>\n",
       "      <th>US_New Year's Day</th>\n",
       "      <th>US_Memorial Day</th>\n",
       "      <th>US_Juneteenth National Independence Day</th>\n",
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       "      <th>US_Columbus Day</th>\n",
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       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2024-08-31</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>2024-09-30</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       month  US_New Year's Day  US_Memorial Day  \\\n",
       "0 2024-05-31                  0                0   \n",
       "1 2024-06-30                  0                0   \n",
       "2 2024-07-31                  0                0   \n",
       "3 2024-08-31                  0                0   \n",
       "4 2024-09-30                  0                0   \n",
       "\n",
       "   US_Juneteenth National Independence Day  US_Independence Day  US_Labor Day  \\\n",
       "0                                        0                    0             0   \n",
       "1                                        1                    0             0   \n",
       "2                                        0                    1             0   \n",
       "3                                        0                    0             0   \n",
       "4                                        0                    0             1   \n",
       "\n",
       "   US_Veterans Day  US_Thanksgiving  US_Christmas Day  \\\n",
       "0                0                0                 0   \n",
       "1                0                0                 0   \n",
       "2                0                0                 0   \n",
       "3                0                0                 0   \n",
       "4                0                0                 0   \n",
       "\n",
       "   US_Martin Luther King Jr. Day  US_Washington's Birthday  US_Columbus Day  \n",
       "0                              0                         0                0  \n",
       "1                              0                         0                0  \n",
       "2                              0                         0                0  \n",
       "3                              0                         0                0  \n",
       "4                              0                         0                0  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nixtla.date_features import CountryHolidays\n",
    "\n",
    "us_holidays = CountryHolidays(countries=['US'])\n",
    "dates = pd.date_range(start=future_df.iloc[0]['month'], end=future_df.iloc[-1]['month'], freq='D')\n",
    "holidays_df = us_holidays(dates)\n",
    "monthly_holidays = holidays_df.resample('M').max()\n",
    "\n",
    "monthly_holidays = monthly_holidays.reset_index(names='month')\n",
    "\n",
    "future_df = future_df.merge(monthly_holidays)\n",
    "\n",
    "future_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f32286cb",
   "metadata": {},
   "source": [
    "We perform the same steps for the input dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a8a2514",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>chocolate</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242</th>\n",
       "      <td>2024-03-31</td>\n",
       "      <td>59</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>243</th>\n",
       "      <td>2024-04-30</td>\n",
       "      <td>51</td>\n",
       "      <td>0</td>\n",
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      ],
      "text/plain": [
       "         month  chocolate  US_New Year's Day  US_New Year's Day (observed)  \\\n",
       "239 2023-12-31         90                  0                             0   \n",
       "240 2024-01-31         64                  1                             0   \n",
       "241 2024-02-29         66                  0                             0   \n",
       "242 2024-03-31         59                  0                             0   \n",
       "243 2024-04-30         51                  0                             0   \n",
       "\n",
       "     US_Memorial Day  US_Independence Day  US_Independence Day (observed)  \\\n",
       "239                0                    0                               0   \n",
       "240                0                    0                               0   \n",
       "241                0                    0                               0   \n",
       "242                0                    0                               0   \n",
       "243                0                    0                               0   \n",
       "\n",
       "     US_Labor Day  US_Veterans Day  US_Thanksgiving  US_Christmas Day  \\\n",
       "239             0                0                0                 1   \n",
       "240             0                0                0                 0   \n",
       "241             0                0                0                 0   \n",
       "242             0                0                0                 0   \n",
       "243             0                0                0                 0   \n",
       "\n",
       "     US_Christmas Day (observed)  US_Martin Luther King Jr. Day  \\\n",
       "239                            0                              0   \n",
       "240                            0                              1   \n",
       "241                            0                              0   \n",
       "242                            0                              0   \n",
       "243                            0                              0   \n",
       "\n",
       "     US_Washington's Birthday  US_Columbus Day  US_Veterans Day (observed)  \\\n",
       "239                         0                0                           0   \n",
       "240                         0                0                           0   \n",
       "241                         1                0                           0   \n",
       "242                         0                0                           0   \n",
       "243                         0                0                           0   \n",
       "\n",
       "     US_Juneteenth National Independence Day  \\\n",
       "239                                        0   \n",
       "240                                        0   \n",
       "241                                        0   \n",
       "242                                        0   \n",
       "243                                        0   \n",
       "\n",
       "     US_Juneteenth National Independence Day (observed)  \n",
       "239                                                  0   \n",
       "240                                                  0   \n",
       "241                                                  0   \n",
       "242                                                  0   \n",
       "243                                                  0   "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Add exogenous features to input dataframe\n",
    "\n",
    "dates = pd.date_range(start=df.iloc[0]['month'], end=df.iloc[-1]['month'], freq='D')\n",
    "holidays_df = us_holidays(dates)\n",
    "monthly_holidays = holidays_df.resample('M').max()\n",
    "\n",
    "monthly_holidays = monthly_holidays.reset_index(names='month')\n",
    "\n",
    "df = df.merge(monthly_holidays)\n",
    "\n",
    "df.tail()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcc34ab7",
   "metadata": {},
   "source": [
    "Great! Now, TimeGPT will consider the holidays as exogenous variables and the upcoming holidays will help it make predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3372c3dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:nixtla.nixtla_client:Validating inputs...\n",
      "INFO:nixtla.nixtla_client:Preprocessing dataframes...\n",
      "INFO:nixtla.nixtla_client:Inferred freq: M\n",
      "WARNING:nixtla.nixtla_client:The specified horizon \"h\" exceeds the model horizon. This may lead to less accurate forecasts. Please consider using a smaller horizon.\n",
      "INFO:nixtla.nixtla_client:Using the following exogenous variables: US_New Year's Day, US_Memorial Day, US_Juneteenth National Independence Day, US_Independence Day, US_Labor Day, US_Veterans Day, US_Thanksgiving, US_Christmas Day, US_Martin Luther King Jr. Day, US_Washington's Birthday, US_Columbus Day\n",
      "INFO:nixtla.nixtla_client:Calling Forecast Endpoint...\n"
     ]
    }
   ],
   "source": [
    "fcst_df = nixtla_client.forecast(\n",
    "    df=df,\n",
    "    h=14,\n",
    "    freq='M',\n",
    "    time_col='month',\n",
    "    target_col='chocolate',\n",
    "    X_df=future_df\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "31de98bf",
   "metadata": {},
   "source": [
    "> 📘 Available models in Azure AI\n",
    ">\n",
    "> If you are using an Azure AI endpoint, please be sure to set `model=\"azureai\"`:\n",
    ">\n",
    "> `nixtla_client.forecast(..., model=\"azureai\")`\n",
    "> \n",
    "> For the public API, we support two models: `timegpt-1` and `timegpt-1-long-horizon`. \n",
    "> \n",
    "> By default, `timegpt-1` is used. Please see [this tutorial](https://docs.nixtla.io/docs/tutorials-long_horizon_forecasting) on how and when to use `timegpt-1-long-horizon`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "48a845e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2400x350 with 1 Axes>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nixtla_client.plot(\n",
    "    df, \n",
    "    fcst_df, \n",
    "    time_col='month',\n",
    "    target_col='chocolate',\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b75b4388",
   "metadata": {},
   "source": [
    "We can then plot the weights of each holiday to see which are more important in forecasing the interest in chocolate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "746e3143",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: ylabel='features'>"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nixtla_client.weights_x.plot.barh(x='features', y='weights', figsize=(10, 10))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b0c17ee3-e9a1-45de-b2e1-2b8b0094bb31",
   "metadata": {},
   "source": [
    "\n",
    "Here's a breakdown of how the `date_features` parameter works:\n",
    "\n",
    "- **`date_features` (bool or list of str or callable)**: This parameter specifies which date attributes to consider.\n",
    "    - If set to `True`, the model will automatically add the most common date features related to the frequency of the given dataframe (`df`). For a daily frequency, this could include features like day of the week, month, and year.\n",
    "    - If provided a list of strings, it will consider those specific date attributes. For example, `date_features=['weekday', 'month']` will only add the day of the week and month as features.\n",
    "    - If provided a callable, it should be a function that takes dates as input and returns the desired feature. This gives flexibility in computing custom date features.\n",
    "\n",
    "- **`date_features_to_one_hot` (bool or list of str)**: After determining the date features, one might want to one-hot encode them, especially if they are categorical in nature (like weekdays). One-hot encoding transforms these categorical features into a binary matrix, making them more suitable for many machine learning algorithms.\n",
    "    - If `date_features=True`, then by default, all computed date features will be one-hot encoded.\n",
    "    - If provided a list of strings, only those specific date features will be one-hot encoded.\n",
    "\n",
    "By leveraging the `date_features` and `date_features_to_one_hot` parameters, one can efficiently incorporate the temporal effects of date attributes into their forecasting model, potentially enhancing its accuracy and interpretability."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "python3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
